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A dataset for benchmarking keyphrase extraction and generation techniques from long document English scientific papers. For more details about the dataset please refer the original paper - [](). |
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Data source - []() |
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## Dataset Summary |
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## Dataset Structure |
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### Data Fields |
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- **id**: unique identifier of the document. |
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- **sections**: list of all the sections present in the document. |
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- **sec_text**: list of white space separated list of words present in each section. |
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- **sec_bio_tags**: list of BIO tags of white space separated list of words present in each section. |
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- **extractive_keyphrases**: List of all the present keyphrases. |
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- **abstractive_keyphrase**: List of all the absent keyphrases. |
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### Data Splits |
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|Split| #datapoints | |
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|--|--| |
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| Train-Small | 20,000 | |
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| Train-Medium | 50,000 | |
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| Train-Large | 90,019 | |
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| Test | 3413 | |
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| Validation | 3339 | |
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## Usage |
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### Small Dataset |
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```python |
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from datasets import load_dataset |
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# get small dataset |
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dataset = load_dataset("midas/ldkp3k", "small") |
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def order_sections(sample): |
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""" |
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corrects the order in which different sections appear in the document. |
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resulting order is: title, abstract, other sections in the body |
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""" |
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sections = [] |
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sec_text = [] |
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sec_bio_tags = [] |
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if "title" in sample["sections"]: |
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title_idx = sample["sections"].index("title") |
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sections.append(sample["sections"].pop(title_idx)) |
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sec_text.append(sample["sec_text"].pop(title_idx)) |
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sec_bio_tags.append(sample["sec_bio_tags"].pop(title_idx)) |
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if "abstract" in sample["sections"]: |
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abstract_idx = sample["sections"].index("abstract") |
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sections.append(sample["sections"].pop(abstract_idx)) |
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sec_text.append(sample["sec_text"].pop(abstract_idx)) |
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sec_bio_tags.append(sample["sec_bio_tags"].pop(abstract_idx)) |
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sections += sample["sections"] |
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sec_text += sample["sec_text"] |
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sec_bio_tags += sample["sec_bio_tags"] |
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return sections, sec_text, sec_bio_tags |
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# sample from the train split |
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print("Sample from train data split") |
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train_sample = dataset["train"][0] |
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sections, sec_text, sec_bio_tags = order_sections(train_sample) |
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print("Fields in the sample: ", [key for key in train_sample.keys()]) |
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print("Section names: ", sections) |
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print("Tokenized Document: ", sec_text) |
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print("Document BIO Tags: ", sec_bio_tags) |
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print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"]) |
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print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"]) |
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print("\n-----------\n") |
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# sample from the validation split |
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print("Sample from validation data split") |
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validation_sample = dataset["validation"][0] |
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sections, sec_text, sec_bio_tags = order_sections(validation_sample) |
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print("Fields in the sample: ", [key for key in validation_sample.keys()]) |
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print("Section names: ", sections) |
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print("Tokenized Document: ", sec_text) |
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print("Document BIO Tags: ", sec_bio_tags) |
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print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"]) |
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print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"]) |
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print("\n-----------\n") |
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# sample from the test split |
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print("Sample from test data split") |
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test_sample = dataset["test"][0] |
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sections, sec_text, sec_bio_tags = order_sections(test_sample) |
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print("Fields in the sample: ", [key for key in test_sample.keys()]) |
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print("Section names: ", sections) |
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print("Tokenized Document: ", sec_text) |
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print("Document BIO Tags: ", sec_bio_tags) |
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print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"]) |
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print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"]) |
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print("\n-----------\n") |
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``` |
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**Output** |
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```bash |
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``` |
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### Medium Dataset |
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```python |
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from datasets import load_dataset |
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# get medium dataset |
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dataset = load_dataset("midas/ldkp3k", "medium") |
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``` |
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### Large Dataset |
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```python |
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from datasets import load_dataset |
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# get large dataset |
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dataset = load_dataset("midas/ldkp3k", "large") |
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``` |
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## Citation Information |
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Please cite the works below if you use this dataset in your work. |
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``` |
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@article{mahata2022ldkp, |
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title={LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents}, |
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author={Mahata, Debanjan and Agarwal, Naveen and Gautam, Dibya and Kumar, Amardeep and Parekh, Swapnil and Singla, Yaman Kumar and Acharya, Anish and Shah, Rajiv Ratn}, |
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journal={arXiv preprint arXiv:2203.15349}, |
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year={2022} |
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} |
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``` |
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``` |
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@article{lo2019s2orc, |
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title={S2ORC: The semantic scholar open research corpus}, |
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author={Lo, Kyle and Wang, Lucy Lu and Neumann, Mark and Kinney, Rodney and Weld, Dan S}, |
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journal={arXiv preprint arXiv:1911.02782}, |
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year={2019} |
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} |
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``` |
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``` |
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@inproceedings{ccano2019keyphrase, |
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title={Keyphrase generation: A multi-aspect survey}, |
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author={{\c{C}}ano, Erion and Bojar, Ond{\v{r}}ej}, |
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booktitle={2019 25th Conference of Open Innovations Association (FRUCT)}, |
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pages={85--94}, |
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year={2019}, |
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organization={IEEE} |
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} |
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``` |
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``` |
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@article{meng2017deep, |
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title={Deep keyphrase generation}, |
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author={Meng, Rui and Zhao, Sanqiang and Han, Shuguang and He, Daqing and Brusilovsky, Peter and Chi, Yu}, |
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journal={arXiv preprint arXiv:1704.06879}, |
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year={2017} |
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} |
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``` |
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## Contributions |
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Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax), [@UmaGunturi](https://github.com/UmaGunturi) and [@ad6398](https://github.com/ad6398) for adding this dataset |
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